Chapter 5 Spatial Data and Maps
The Spatial section of this book adds the spatial dimension and geographic data science. Most environmental systems are significantly affected by location, so the geographic perspective is highly informative. In this chapter, we’ll explore the basics of
- building and using feature-based (vector) data using the
sf(simple features) package - raster data using the
terrapackage - some common mapping methods in
plotandggplot2
Especially for the feature data, which is built on a database model (with geometry as one variable), the tools we’ve been learning in dplyr and tidyr will also be useful to working with attributes.
Caveat: Spatial data and analysis is very useful for environmental data analysis, and we’ll only scratch the surface. Readers are encouraged to explore other resources on this important topic, such as:
- Geocomputation with R at https://geocompr.robinlovelace.net/
- Simple Features for R at https://r-spatial.github.io/sf/
- Spatial Data Science with R at https://rspatial.org
5.1 Spatial Data
Spatial data are data using a Cartesian coordinate system with x, y, z, and maybe more dimensions. Geospatial data are spatial data that we can map on our planet and relate to other geospatial data based on geographic coordinate systems (GCS) of longitude and latitude or known projections to planar coordinates like Mercator, Albers, or many others. You can also use local coordinate systems with the methods we’ll learn, to describe geometric objects or a local coordinate system to “map out” an archaeological dig or describe the movement of ants on an anthill, but we’ll primarily use geospatial data.
To work with spatial data requires extending R to deal with it using packages. Many have been developed, but the field is starting to mature using international open GIS standards. We’ll look at two, one for features, the other including rasters, and you’ll need to install each with install.packages("sf") and install.packages("terra").
sf (Simple Features)
- Feature-based (vector) methods
- ISO 19125 standard for GIS geometries
- Has functions for working with spatial data
- Doesn’t need many additional packages, though you may still need
rgdalinstalled for some tools you want to use - Works with ggplot2 and tmap for nice looking maps
- Cheat sheet and other information at https://r-spatial.github.io/sf/
- Earliest CRAN archive is 0.2 on 2016-10-26, 1.0 not until 2021-06-29
- Author: Edzer Pebesma
terra
- Intended to eventually replace
raster, by the same author (Hijmans) - CRAN: “Methods for spatial data analysis with raster and vector data. Raster methods allow for low-level data manipulation as well as high-level global, local, zonal, and focal computation. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on https://rspatial.org/terra/ to get started.”
5.1.1 Simple geometry building in sf and terra
We won’t explore this here, but the Introduction to Environmental Data Science book https://bookdown.org/igisc/EnvDataSci/ includes methods for building sf and terra features from geometries. We’ll skip to using points in data frames and shapefiles.
5.1.2 Building points from a data frame
We’re going to take a different approach with points, since if we’re building them from scratch they commonly come in the form of a data frame with x and y columns, so the st_as_sf is a nice simple approach to bringing in points with coordinates stored in a data frame, a very common situation.
The sf::st_as_sf function is useful for a lot of things, and follows the convention of R’s many “as” functions: if it can understand the input as having “space and time” (st) data, it can convert it to an sf.
We’ll start by creating a data frame representing 12 Sierra Nevada (and some adjacent) weather stations, with variables entered as vectors, including attributes. The order of vectors needs to be the same for all, where the first point has index 1, etc.
sta <- c("OROVILLE","AUBURN","PLACERVILLE","COLFAX","NEVADA CITY","QUINCY",
"YOSEMITE","PORTOLA","TRUCKEE","BRIDGEPORT","LEE VINING","BODIE")
long <- c(-121.55,-121.08,-120.82,-120.95,-121.00,-120.95,
-119.59,-120.47,-120.17,-119.23,-119.12,-119.01)
lat <- c(39.52,38.91,38.70,39.09,39.25,39.94,
37.75,39.81,39.33,38.26,37.96,38.21)
elev <- c(52,394,564,725,848,1042,
1225,1478,1775,1972,2072,2551)
temp <- c(10.7,9.7,9.2,7.3,6.7,4.0,
5.0,0.5,-1.1,-2.2,0.4,-4.4)
prec <- c(124,160,171,207,268,182,
169,98,126,41,72,40)To build the sf data, we start by building the data frame…
df <- data.frame(sta,elev,temp,prec,long,lat)… then use the very handy sf::st_as_sf function to make an sf out of it, using the long and lat as a source of coordinates (Figure 5.1).
wst <- st_as_sf(df, coords=c("long","lat"), crs=4326)
ggplot(data=wst) + geom_sf(mapping = aes(col=elev))
FIGURE 5.1: Points created from a dataframe with Simple Features
5.1.3 Creating features from shapefiles
Both sf’s st_read and terra’s vect read the open-GIS shapefile format developed by Esri for points, polylines, and polygons. You would normally have shapefiles (and all the files that go with them – .shx, etc.) stored on your computer, but we’ll access one from the igisci external data folder, and use that ex() function we used earlier with CSVs. Remember that we could just include that and the library calls just once at the top of our code like this…
library(igisci)
library(sf)If we just send a spatial dataset like an sf spatial data frame to the plot system, it will plot all of the variables by default (Figure 5.2).
BayAreaCounties <- st_read(ex("BayArea/BayAreaCounties.shp"))plot(BayAreaCounties)
FIGURE 5.2: A simple plot of polygon data by default shows all variables
But with just one variable, of course, it just produces a single map (Figure 5.3).
plot(BayAreaCounties["POPULATION"])
FIGURE 5.3: A single map with a legend is produced when a variable is specified
Notice that in the above map, we used the [] accessor. Why didn’t we use the simple $ accessor? Remember that plot() figures out what to do based on what you provide it. And there’s an important difference in what you get with the two accessors, which we can check with class():
class(BayAreaCounties["POPULATION"])## [1] "sf" "data.frame"class(BayAreaCounties$POPULATION)## [1] "numeric"You might see that what you get with plot(BayAreaCounties$POPULATION) is not very informative, since the object is just a numeric vector, while using [] accessor returns a spatial dataframe.
There’s a lot more we could do with the base R plot system, so we’ll learn some of these before exploring what we can do with ggplot2, tmap, and leaflet. But first we need to learn more about building geospatial data.
We’ll use st_as_sf() for that, but we’ll need to specify the coordinate referencing system (CRS), in this case GCS. We’ll only briefly explore how to specify the CRS here. For a thorough coverage, please see Lovelace, Nowosad, and Muenchow (2019).
5.2 Coordinate Referencing Systems
Before we try the next method for bringing in spatial data – converting data frames – we need to look at coordinate referencing systems (CRS). First, there are quite a few, with some spherical like the geographic coordinate system (GCS) of longitude and latitude, and others planar projections of GCS using mathematically defined projections such as Mercator, Albers Conformal Conic, Azimuthal, etc., and including widely used government-developed systems such as UTM (universal transverse mercator) or state plane. Even for GCS, there are many varieties since geodetic datums can be chosen, and for very fine resolution work where centimetres or even millimetres matter, this decision can be important (and tectonic plate movement can play havoc with tying it down.)
There are also multiple ways of expressing the CRS, either to read it or to set it. The full specification of a CRS can be displayed for data already in a CRS, with either sf::st_crs or terra::crs.
st_crs(CA_counties)## Coordinate Reference System:
## User input: WGS 84
## wkt:
## GEOGCRS["WGS 84",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["latitude",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["longitude",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4326]]… though there are other ways to see the crs in shorter forms, or its individual properties :
st_crs(CA_counties)$proj4string## [1] "+proj=longlat +datum=WGS84 +no_defs"st_crs(CA_counties)$units_gdal## [1] "degree"st_crs(CA_counties)$epsg## [1] 4326So, to convert the sierra data into geospatial data with st_as_sf, we might either do it with the reasonably short PROJ format …
GCS <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
wsta = st_as_sf(sierraFeb, coords = c("LONGITUDE","LATITUDE"), crs=GCS)… or with the even shorter EPSG code (which we can find by Googling), which can either be provided as text "epsg:4326" or often just the number, which we’ll use next.
5.3 Creating sf Data from Data Frames
As we saw earlier, if your data frame has geospatial coordinates like LONGITUDE and LATITUDE …
names(sierraFeb)## [1] "STATION_NAME" "COUNTY" "ELEVATION" "LATITUDE"
## [5] "LONGITUDE" "PRECIPITATION" "TEMPERATURE"… we have what we need to create geospatial data from it. Earlier we read in a series of vectors built in code with c() functions from 12 selected weather stations; this time we’ll use a data frame that has all of the Sierra Nevada weather stations (Figure 5.4).
wsta <- st_as_sf(sierraFeb, coords = c("LONGITUDE","LATITUDE"), crs=4326)
ggplot(data=wsta) + geom_sf(aes(col=ELEVATION))
FIGURE 5.4: Points created from data frame with coordinate variables
5.4 Building a 2-state map from geometry
Let’s briefly look at some code that builds two state boundary features with attributes from a set of GCS coordinates entered as vectors of coordinate pairs, using sf geometry-building methods, with simple attribues added using bind_rows().
Feel free to just copy this and run it. We aren’t going to go over these methods in this 2-day course, but building geometries this way is covered in https://bookdown.org/igisc/EnvDataSci/.
library(sf); library(tidyverse)
CA_matrix <- rbind(c(-124,42),c(-120,42),c(-120,39),c(-114.5,35),
c(-114.1,34.3),c(-114.6,32.7),c(-117,32.5),c(-118.5,34),c(-120.5,34.5),
c(-122,36.5),c(-121.8,36.8),c(-122,37),c(-122.4,37.3),c(-122.5,37.8),
c(-123,38),c(-123.7,39),c(-124,40),c(-124.4,40.5),c(-124,41),c(-124,42))
NV_matrix <- rbind(c(-120,42),c(-114,42),c(-114,36),c(-114.5,36),
c(-114.5,35),c(-120,39),c(-120,42))
CA_list <- list(CA_matrix); NV_list <- list(NV_matrix)
CA_poly <- st_polygon(CA_list); NV_poly <- st_polygon(NV_list)
sfc_2states <- st_sfc(CA_poly,NV_poly,crs=4326) # crs=4326 specifies GCS
# st_geometry_type(sfc_2states)
attributes <- bind_rows(c(abb="CA", area=423970, pop=39.56e6),
c(abb="NV", area=286382, pop=3.03e6)) %>%
mutate(area=as.numeric(area),pop=as.numeric(pop))
twostates <- st_sf(attributes, geometry = sfc_2states)5.5 Using maptiles to create a basemap
For vector data, it’s often nice to display over a basemap by accessing raster tiles that are served on the internet by various providers. We’ll use the maptiles package to try displaying the CA and NV boundaries we created earlier. The maptiles package supports a variety of basemap providers, and I’ve gotten the following to work: "OpenStreetMap", "Stamen.Terrain", "Stamen.TerrainBackground", "Esri.WorldShadedRelief", "Esri.NatGeoWorldMap", "Esri.WorldGrayCanvas", "CartoDB.Positron", "CartoDB.Voyager", "CartoDB.DarkMatter", "OpenTopoMap", "Wikimedia", however, they don’t work at all scales and locations – you’ll often see an Error in grDevices, if so then try another provider – the default "OpenStreetMap" seems to work the most reliably (Figure 5.5).
library(terra); library(maptiles)
# Get the raster that covers the extent of CANV:
calnevaBase <- get_tiles(twostates, provider="OpenTopoMap")
st_crs(twostates)$epsg## [1] 4326plotRGB(calnevaBase) # starts plot with a raster basemap
lines(vect(twostates), col="black", lwd=2)
FIGURE 5.5: Using maptiles for a base map
In the code above, we can also see the use of terra functions and parameters. Learn more about these by reviewing the code and considering:
terra functions: The terra::vect() function creates a SpatVector that works with terra::lines to display the boundary lines in plot(). (More on terra later, in the Raster section.)
parameters: Note the use of the parameter lwd (line width) from the plot system. This is one of many parameter settings described in ?par. It defaults to 1, so 2 makes it twice as thick. You could also use lty (line type) to change it to a dashed line with lty="dashed" or lty=2.
And for the sierraFeb data, we’ll start with st_as_sf and the coordinate system (4326 for GCS), then use a maptiles basemap again, and the terra::points method to add the points (Figure 5.6).
library(terra); library(maptiles)
sierraFebpts <- st_as_sf(sierraFeb, coords = c("LONGITUDE", "LATITUDE"), crs=4326)
sierraBase <- get_tiles(sierraFebpts)
st_crs(sierraFebpts)$epsg## [1] 4326plotRGB(sierraBase)
points(vect(sierraFebpts))
FIGURE 5.6: Converted sf data for map with tiles
5.6 Raster data
Simple Features are feature-based, of course, so it’s not surprising that sf doesn’t have support for rasters. So we’ll want to either use the raster package or its imminent replacement, terra (which also has vector methods – see Hijmans (n.d.) and Lovelace, Nowosad, and Muenchow (2019)), and I’m increasingly using terra since it has some improvements that I find useful.
5.6.1 Building rasters
We can start by building one from scratch. More explanation on this method is in https://bookdown.org/igisc/EnvDataSci/, but here’s a quick look. The terra package has a rast() function for this.
library(terra)
new_ras <- rast(ncol = 12, nrow = 6,
xmin = -180, xmax = 180, ymin = -90, ymax = 90,
vals = 1:72)
names(new_ras) <- "world30deg"Simple feature data or SpatVectors can be plotted along with rasters using terra’s lines, polys, or points functions. Here we’ll use the twostates sf we created earlier (make sure to run that code again if you need it). Look closely at the map for our two states (Figure 5.7).
plot(new_ras, main=paste("CANV on",new_ras@ptr$names))
CANV <- vect(twostates)
lines(CANV)
FIGURE 5.7: Simple plot of a worldwide SpatRaster of 30-degree cells, with SpatVector of CA and NV added
5.6.2 Vector to raster conversion
To convert rasters to vectors requires having a template raster with the desired cell size and extent, or an existing raster we can use as a template – we ignore what the values are – such as elev.tif in the following example (Figure 5.8):
streams <- vect(ex("marbles/streams.shp"))
elev <- rast(ex("marbles/elev.tif"))
streamras <- rasterize(streams,elev)
plot(streamras, col="blue")
FIGURE 5.8: Stream raster converted from stream features, with 30 m cells from an elevation raster template
5.6.2.1 What if we have no raster to use as a template?
There’s a way. See the coverage of this in https://bookdown.org/igisc/EnvDataSci/ .
5.6.2.2 Plotting some existing downloaded raster data
Let’s plot some Shuttle Radar Topography Mission (SRTM) elevation data for the Virgin River Canyon at Zion National Park (Figure 5.9), from Jakub Nowosad’s spDataLarge repository, which we’ll need to first install with:
install.packages("spDataLarge",repos="https://nowosad.github.io/drat/",type="source")library(terra)
plot(rast(system.file("raster/srtm.tif", package="spDataLarge")))
FIGURE 5.9: Shuttle Radar Topography Mission (SRTM) image of Virgin River Canyon area, southern Utah
5.7 ggplot2 for Maps
The Grammar of Graphics is the gg of ggplot.
- Key concept is separating aesthetics from data
- Aesthetics can come from variables (using aes()setting) or be constant for the graph
Mapping tools that follow this lead
- ggplot, as we have seen, and it continues to be enhanced (Figure 5.10)
- tmap (Thematic Maps) https://github.com/mtennekes/tmap Tennekes, M., 2018, tmap: Thematic Maps in R, Journal of Statistical Software 84(6), 1-39
ggplot(CA_counties) + geom_sf()
FIGURE 5.10: simple ggplot map
Try ?geom_sf and you’ll find that its first parameter is mapping with aes() by default. The data property is inherited from the ggplot call, but commonly you’ll want to specify data=something in your geom_sf call.
Another simple ggplot, with labels
Adding labels is also pretty easy using aes() (Figure 5.11).
ggplot(CA_counties) + geom_sf() +
geom_sf_text(aes(label = NAME), size = 1.5)
FIGURE 5.11: labels added
And now with fill color, repositioned legend, and no “x” or “y” labels
The x and y labels are unnecessary since the graticule is provided, and for many maps there’s a better place to put the legend than what happens by default – for California’s shape, the legend goes best in Nevada (Figure 5.12).
ggplot(CA_counties) + geom_sf(aes(fill=MED_AGE)) +
geom_sf_text(aes(label = NAME), col="white", size=1.5) +
theme(legend.position = c(0.8, 0.8)) +
labs(x="",y="")
FIGURE 5.12: repositioned legend
Map in ggplot2, zoomed into two counties
We can zoom into two counties by accessing the extent of an existing spatial dataset using st_bbox() (Figure 5.13).
library(tidyverse); library(sf); library(igisci)
census <- st_make_valid(BayAreaTracts) %>%
filter(CNTY_FIPS %in% c("013", "095"))
TRI <- read_csv(ex("TRI/TRI_2017_CA.csv")) %>%
st_as_sf(coords = c("LONGITUDE", "LATITUDE"), crs=4326) %>%
st_join(census) %>%
filter(CNTY_FIPS %in% c("013", "095"),
(`5.1_FUGITIVE_AIR` + `5.2_STACK_AIR`) > 0)
bnd = st_bbox(census)
ggplot() +
geom_sf(data = BayAreaCounties, aes(fill = NAME)) +
geom_sf(data = census, color="grey40", fill = NA) +
geom_sf(data = TRI) +
coord_sf(xlim = c(bnd[1], bnd[3]), ylim = c(bnd[2], bnd[4])) +
labs(title="Census Tracts and TRI air-release sites") +
theme(legend.position = "none")
FIGURE 5.13: Using bbox to zoom into two counties
5.8 tmap
The tmap package provides some nice cartographic capabilities. We’ll use some of its capabilities, but for more thorough coverage, see the “Making maps with R” section of Geocomputation with R (Lovelace, Nowosad, and Muenchow (2019)).
The basic building block is tm_shape(data) followed by various layer elements, such as tm_fill(). The tm_shape function can work with either features or rasters.
Rasters can be displayed in ggplot2, however it’s a bit clumsy so I prefer tmap when you’re using rasters.
library(spData); library(tmap)
m <- tm_shape(world) + tm_fill() + tm_borders() But we’ll make a better map (Figure 5.14) by inserting a graticule before filling the polygons, then use tm_layout for some useful cartographic settings: make the background light blue and avoid excessive inner margins.
library(spData); library(tmap)
bounds <- st_bbox(world)
m <- tm_shape(world, bbox=bounds) + tm_graticules(col="seashell2") +
tm_fill() + tm_borders() + tm_layout(bg.color="lightblue", inner.margins=0)
m
FIGURE 5.14: tmap of the world
Experiment by leaving settings out to see the effect, and explore other options with
?tm_layout, etc. The tmap package has a wealth of options, just a few of which we’ll explore. For instance, we might want to use a different map projection than the default.
Color by variable
As with plot and ggplot2, we can reference a variable to provide a range of colors for features we’re plotting, such as coloring polygon fills to create a choropleth map (Figure 5.15).
library(sf); library(igisci)
library(tmap)
tm_shape(st_make_valid(BayAreaTracts)) + tm_graticules(col="#e5e7e9") + tm_fill(col = "MED_AGE")
FIGURE 5.15: tmap fill colored by variable
tmap of sierraFeb with hillshade and point symbols
We’ll use a raster hillshade as a basemap using tm_raster, zoom into an area with a bounding box (from st_bbox), include county boundaries with tm_borders, and color station points with temperatures with tm_symbols (Figure 5.16).
library(terra)
tmap_mode("plot")
tmap_options(max.categories = 8)
sierra <- st_as_sf(sierraFeb, coords = c("LONGITUDE", "LATITUDE"), crs = 4326)
hillsh <- rast(ex("CA/ca_hillsh_WGS84.tif")) # alt: hillsh <- raster(...)
bounds <- st_bbox(sierra)
tm_shape(hillsh,bbox=bounds)+
tm_graticules(col="azure2") +
tm_raster(palette="-Greys",legend.show=FALSE,n=10) +
tm_shape(sierra) +
tm_symbols(col="TEMPERATURE", palette=c("blue","red"), style="cont",n=8) +
tm_shape(st_make_valid(CA_counties)) + tm_borders() +
tm_legend() +
tm_layout(legend.position=c("RIGHT","TOP"))
FIGURE 5.16: hillshade, borders and point symbols in tmap
Color (rgb) basemap
Earlier we looked at creating a basemap using maptiles. These are also supported in tmap using the tm_rgb (Figure 5.17). Note that here the graticule is covered by the raster tiles, as it was by polygons earlier. We could have put it after the basemap tiles, but it doesn’t look good, but works ok just showing on the edges.
library(tmap); library(maptiles)
# The following gets the raster that covers the extent of CA & NV [twostates]
calnevaBase <- get_tiles(twostates, provider="OpenTopoMap")# g
tm_shape(calnevaBase) + tm_graticules() + tm_rgb() +
tm_shape(twostates) + tm_borders(lwd=3)
FIGURE 5.17: Two western states with a basemap in tmap
5.9 Interactive Maps
The word “static” in “static maps” isn’t something you would have heard in a cartography class thirty years ago, since essentially all maps then were static. Very important in designing maps was, and still is, considering your audience and their perception of symbology.
- Figure-to-ground relationships assume “ground” is a white piece of paper (or possibly a standard white background in a pdf), so good cartographic color schemes tend to range from light for low values to dark for high values.
- Scale is fixed, and there are no “tools” for changing scale, so a lot of attention must be paid to providing scale information.
- Similarly, without the ability to see the map at different scales, inset maps are often needed to provide context.
Interactive maps change the game in having tools for changing scale and always being “printed” on a computer or device where the color of the background isn’t necessarily white. We are increasingly used to using interactive maps on our phones or other devices, and often get frustrated not being able to zoom into a static map. However, as we’ll see, there are trade-offs in that interactive maps don’t provide the same level of control on symbology for what we want to put on the map, but instead depend a lot on basemaps for much of the cartographic design, generally limiting the symbology of data being mapped on top of it.
5.9.1 Leaflet
We’ll come back to tmap to look at its interactive option, but we should start with a very brief look at the package that it uses when you choose interactive mode: leaflet. The R leaflet library itself translates to Javascript and its Leaflet library, which was designed to support “mobile-friendly interactive maps” (https://leafletjs.com). We’ll also look at another R package that translates to leaflet: mapview.
Interactive maps tend to focus on using basemaps for most of the cartographic design work, and quite a few are available. The only code required to display the basemap is addTiles(), which will display the default OpenStreetMap in the area where you provide any features, typically with addMarkers(). This default basemap is pretty good to use for general purposes, especially in urban areas where OpenStreetMap contributors (including a lot of former students I think) have provided a lot of data.
You can specify additional choices using addProviderTiles(), and use a layers control with the choices provided as baseGroups. You have access to all of the basemap provider names with the vector providers, and to see what they look like in your area, explore http://leaflet-extras.github.io/leaflet-providers/preview/index.html, where you can zoom into an area of interest and select the base map to see how it would look in a given zoom (Figure 5.18).
library(leaflet)
leaflet() %>%
addTiles(group="OpenStreetMap") %>%
addProviderTiles("OpenTopoMap",group="OpenTopoMap") %>%
addProviderTiles("Esri.NatGeoWorldMap",group="Esri.NatGeoWorldMap") %>%
addProviderTiles("Esri.WorldImagery",group="Esri.WorldImagery") %>%
addMarkers(lng=-122.4756, lat=37.72222,
popup="Institute for Geographic Information Science, SFSU") %>%
addLayersControl(
baseGroups = c("OpenStreetMap","OpenTopoMap",
"Esri.WorldImagery","Esri.NatGeoWorldMap"))FIGURE 5.18: Leaflet map showing the location of the SFSU Institute for Geographic Information Science with choices of basemaps
With an interactive map, we do have the advantage of a good choice of base maps and the ability to resize and explore the map, but symbology is more limited, mostly just color and size, with only one variable in a legend. Note that interactive maps display in the Viewer window of RStudio or in R Markdown code output as you see in the html version of this book.
For a lot more information on the leaflet package in R, see:
5.9.2 tmap (view mode)
You can change to an interactive mode with tmap by using tmap_mode("view") so you might think that you can do all the great things that tmap does in normal plot mode here, but as we’ve seen, interactive maps don’t provide the same level of symbology. The view mode of tmap, like mapview, is just a wrapper around leaflet, so we’ll focus on the latter. The key parameter needed is tmap_mode, which must be set to "view" to create an interactive map.
library(tmap); library(sf)
tmap_mode("view")
tmap_options(max.categories = 8)
sierra <- st_as_sf(sierraFeb, coords = c("LONGITUDE", "LATITUDE"), crs = 4326)
bounds <- st_bbox(sierra)
#sierraMap <-
tm_basemap(leaflet::providers$Esri.NatGeoWorldMap) +
tm_shape(sierra) + tm_symbols(col="TEMPERATURE",
palette=c("blue","red"), style="cont",n=8,size=0.2) +
tm_legend() +
tm_layout(legend.position=c("RIGHT","TOP"))#sierraMap
#tmap_leaflet(sierraMap)One nice feature of tmap view mode (and mapview) is the ability to select the basemap interactively using the layers symbol. There are lots of basemap available with leaflet (and thus with tmap view). To explore them, see http://leaflet-extras.github.io/leaflet-providers/preview/index.html but recognize that not all of these will work everywhere; many are highly localized or may only work at certain scales.
The following map using tmap could presumably be coded in leaflet, but tmap makes it a lot easier. We’ll use it to demonstrate picking the basemap, which really can help in communicating our data with context (Figure ??).
library(sf); library(tmap); library(igisci)
tmap_mode = "view"
tmap_options(basemaps=c(Topo="OpenTopoMap", Imagery = "Esri.WorldImagery",
NatGeo="Esri.NatGeoWorldMap"))
soilCO2 <- st_read(ex("marbles/co2july95.shp"))
geology <- st_read(ex("marbles/geology.shp"))mblCO2map <- tm_basemap() +
tm_shape(geology) + tm_fill(col="CLASS", alpha=0.5) +
tm_shape(soilCO2) + tm_symbols(col="CO2_",
palette="viridis", style="cont",n=8,size=0.6) +
tm_legend() +
tm_layout(legend.position=c("RIGHT","TOP"))
mblCO2map5.9.3 Interactive mapping of individual penguins abstracted from a big dataset
In a study of spatial foraging patterns by Adélie penguins (Pygoscelis adeliae), Ballard et al. (2019) collected data over five austral summer seasons (parts of December and January in 2005-06, 2006-07, 2007-08, 2008-09, 2012-13) period using SPLASH tags that combine Argos satellite tracking with a time-depth recorder, with 11,192 observations. The 24 SPLASH tags were re-used over the period of the project with a total of 162 individual breeding penguins, each active over a single season.
Our code takes advantage of the abstraction capabilities of base R and dplyr to select an individual penguin from the large data set and prepare its variables for useful display. Then the interactive mode of tmap works well for visualizing the penguin data – both the cloud of all observations and the focused individual – since we can zoom in to see the locations in context, and basemaps can be chosen. The tmap code colors the individual penguin’s locations based on a decimal day derived from day and time. While interactive view is more limited in options, it does at least support a legend and title (Figure 5.19).
library(sf); library(tmap); library(tidyverse); library(igisci)
sat_table <- read_csv(ex("penguins/sat_table_splash.csv"))
obs <- st_as_sf(filter(sat_table,include==1), coords=c("lon","lat"), crs=4326)
uniq_ids <- unique(obs$uniq_id) # uniq_id identifies an individual penguin
onebird <- obs %>%
filter(uniq_id==uniq_ids[sample.int(length(uniq_ids), size=1)]) %>%
mutate(decimalDay = as.numeric(day) + as.numeric(hour)/24)tmap_mode = "view"
tmap_options(basemaps=c(Terrain = "Esri.WorldTerrain",
Imagery = "Esri.WorldImagery",
OceanBasemap = "Esri.OceanBasemap",
Topo="OpenTopoMap",
Ortho="GeoportailFrance.orthos"))
penguinMap <- tm_basemap() +
tm_shape(obs) + tm_symbols(col="gray", size=0.01, alpha=0.5) +
tm_shape(onebird) + tm_symbols(col="decimalDay",
palette="viridis", style="cont",n=8,size=0.6) +
tm_legend() +
tm_layout()
tmap_leaflet(penguinMap)